Testing to see if my data is uniform

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To determine if a set of 16 data points follows a uniform distribution, one can utilize goodness of fit tests, which compare observed data against expected distribution values. While the punif function in R can be used, it may not provide clear results without proper statistical context. Bayesian analysis is recommended for a more comprehensive approach, allowing for the incorporation of prior beliefs about the distribution. This method helps refine estimates based on the data, improving accuracy over time. Ultimately, testing against alternative distributions can also provide insights into the data's characteristics.
Nyasha
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So l have got 16 data points and l would like to know if this data follows a uniform distribution. I have tried using the punif function in R, but l am not sure about the results l am getting. Can someone please tell me what is the best way and hopefully easiest way to see if data is uniformly distributed
 
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It is never possible to say that a given set of data does or does not obey a certain distribution. You can estimate the likelihood of seeing the data you have if you assume that it is uniform, and you can make an estimate of the range of that distribution. But that doesn't tell you how likely it is to be uniform.
The only correct way is through Bayesian analysis. You have to plug in a priori beliefs of what the distribution might be, and how likely each possibility is. Then you can use the data to revise these estimates. The more data, the closer the revision gets to the "truth".
Failing that, I suggest you think up the most likely alternative to uniform (knowing what the data means) and show that the observations fit a uniform distribution better than they fit the alternative.
 
Nyasha said:
So l have got 16 data points and l would like to know if this data follows a uniform distribution. I have tried using the punif function in R, but l am not sure about the results l am getting. Can someone please tell me what is the best way and hopefully easiest way to see if data is uniformly distributed

Hey Nyasha.

One way to perform such a test is through a goodness of fit test.

The way this works intuitively is basically that it compares how 'close' each value of your expected distribution is from your observed and then based on that variation, checks whether under some confidence level using frequentist statistics if you can either reject or fail to reject the hypothesis under that test statistic, whether the observed distribution is the expected distribution.

The Bayesian analysis is a lot more general than this, but as a starting point, you could do this test to get an idea of the similarity and how big a confidence level is needed to fail to reject the hypothesis.

You are using R, so take a look at this:

http://ww2.coastal.edu/kingw/statistics/R-tutorials/goodness.html
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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